School of BEES seminar series
UNSW School of Mathematics and Statistics
18 September 2025
Causal inference attempts to estimate the causal effect of an intervention on a particular outcome. Causal inference involves the careful design and analysis of experiments and observational studies.
The fundamental problem of causal inference is that we only only observe one potential outcome.
Causal inference is a missing data problem.
\[\begin{align*} \tau &= \bar{Y}(1) - \bar{Y}(0) \\ &= \frac{1}{N} \sum_{i=1}^{N} \left[ Y_i(1) - Y_i(0)\right]\\ \end{align*}\]
Randomised experiments
Observational studies
The goal of designing an observational study is to approximate a randomised experiment as closely as possible, and mitigate the effects of confounding. One approach is matching.
Statistical interpretation:
Question: What is the impact of human-induced regeneration (HIR) carbon offset projects on forest cover? (Macintosh et al. 2024)
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